Cognitive development in deaf children: the interface of language and perception in neuropsychology
Bibliographic record
Abstract
What does the sense of hearing contribute to human development? To answer the question, we must ask what the sense of hearing gives the child. Hearing gives the child the acoustic correlates of the physical world: approaching footsteps, dog barks, car horns, and the pitter-patter of rain. Hearing also allows the child to revel in the patterned complexity of a Beethoven symphony or a mother’s lullaby. Children who are born deaf clearly miss a great deal. However, hearing conveys much more to the growing child than the acoustics of the physical world. Hearing is the sensory modality through which children perceive speech — the universe of talk that ties individuals, families and societies together. Children born with bilateral hearing losses that are severe (70–89 dB loss) or profound (>90 dB loss) are referred to as deaf. They cannot hear conversational speech (approximately 60 dB) and consequently do not spontaneously learn to talk. Indeed, not talking at the right age is one of the first signs that a child cannot hear. The primary consequence of childhood deafness is that it blocks the development of spoken language — both the acts of speaking and comprehending. This fact leads us to ask what spoken language contributes to the child’s cognitive development. Be-
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".